1. Methodology
Reclamation of abandoned coal mine sites is an extremely complex process.  Many physical, economic, social and political factors affect the final outcome.  Some of the non-physical variables are not controllable and there is no easy way to quantify them.  The SDSS was designed to support decision making in the context of natural resource management.  Data from various sources were collected and processed to be stored in the GIS database.  A user interface was provided for adding additional information relevant to coal mine reclamation, as the user sees fit.  This SDSS was integrated with the GIS database, with a soil erosion model, and with multi-criteria decision-making (MCDM) methods (Figure 3).

To implement the SDSS and support decision-making, we have to look first into the decision-making process.  The steps of basic decision-making include:
 

    1. Define the alternatives.

      2.  
      This is the process used to identify the problems and the goals to be accomplished through decision-making.  As for XXX reclamation planning, the problem is that there are too many abandoned sites and too limited resources for funding.  The goal is to select the sites most urgently in need and reclaim them using the limited resources.
       
       
    2. List the comparison criteria.

      3.  
      Comparison criteria consist of the available variables that might affect or relate to the problem and need to be taken into consideration.  For simple decision making, the criteria can be listed in a table.  For example, a customer wants to make a decision on choosing a computer from different brands.  The decision-maker needs to XX: first, decide what components or criteria such as memory, speed, size and hard drive are considered; XXX then, acquire the information XX; and finally, list them in a table to make comparisons.  But reclamation is a far more complex process, XXX involving spatial aspects.  Criteria can range from physical, to social, to political data.  XXX The data, therefore, have to be organized in a more systematic manner--a simple table is not sufficient for this kind of problem. Since GIS is the best tool to organize spatial data, it definitely improves XXX processes involving  spatially distributed data.
       
       
    3. Weigh the importance of each criterion.

      4.  
      Not all criteria are created equal--each has its own weight. The importance of each criterion can be identified by its weight. XXX  XXX It is difficult to calculate and compare XXX qualitative data, and therefore the advantage of weighing is most significant. There are three ways to assign a weight to a criterion. One XXX is to give a particular number to each criterion. Another XXX is to set the total weight of all the criteria as 100, and to assign a percentage to each criterion according to its relative importance in the whole criteria set. The third way is a little more complicated and needs some algorithms to generate the final list of weights for each criterion. Pairwise comparison belongs to this category. In a pairwise comparison, each member of a pair of XXX criteria is given a relevant weight and the algorithm is applied to calculate the weight set for all the criteria.
       
       
    4. Rate each alternative of each criterion.

      5.  
      Within each criterion, there is a range of values, if it is quantitative, or a distinct set of categories, if it is qualitative, to describe the data. Each alternative on a criterion is rated by assigning a value or a score according to its original value or category and  depending on the data type. There are several approaches to rating a range of values, for instance a linear method can be used for normally distributed data. Another approach is to classify the range of values into several categories and to assign a value to each category. In this case, all the alternatives belonging to a given category receive the same value.

       

    5. Perform the comparison.
With all the criteria weighted and alternatives in each criterion rated, the total score can be calculated in a straightforward manner. By comparing the final scores of all the alternatives, a decision-maker can choose the most practical one according to theXX preferences and to existing conditions. If the results are not satisfactory, XXX the weighing and rating schema can be re-done.  With a computer XXX program, it is quite easy to accomplish this process.

Decision supporting systems (DSS) provide the environment or tools for the decision-makers to go through all the steps without bothering to understand the system. Decision-makers do not have to write the code for the programs; XXX what they need to do is XXX select criteria, weigh it, and rate it, using the functions that DSS provides. The DSS XXX takes care of the rest:XXX storing the data, organizing the data and models, calculating the scores, and presenting the result in a more reasonable way. In a spatial decision support system for reclamation, a GIS database is used to store and manipulate the data, a model base is used to manage the models for calculation, and multiple criteria analysis is used to form the decision matrix and produce the eventual result.

Developing a GIS Database

The GIS database stores all raw data, processed data, and models. The ability to capture, retrieve, and manipulate complex spatial data can be the key to successful decision making. Each data set, of any particular variable or criterion, forms a data layer in the database. Since data must be collected from various sources, however, before the layers can be overlaid, the data must be referenced to a common geographic coordinate system. The GIS database in this study used a Universal Transform Mercator (UTM) projection system as its coordinate system. There is not much difference in appearance and calculation between different projection systems for a small area like South Fork. The reason that UTM was chosen is that most of the data were originally projected to UTM, such as TM images, streams, and mine sites.XXX Therefore no more effort is needed if they are kept in UTM. Also, unnecessary distortion and loss of information from projection transformation are  avoided. The common map unit for UTM projection is the meter. The data are stored either in the vector or in the raster format, depending on the particular layer's properties.

Vector vs. Raster  [Xiao Liu, Do yu want italics here?]

XXX Vector-based data representXX geographic features similar to the way maps do. Points represent geographic features too small to be depicted as lines or areas; lines represent geographic features too narrow to be depicted as areas; and areas represent homogeneous geographic features. A Cartesian (x, y) coordinate system references real-world locations. In a vector-based data model, each location is recorded as a single x, y coordinate. Points are recorded as a single coordinate. Lines are recorded as a series of ordered x, y coordinates. Areas are recorded as a series of x, y coordinates defining line segments that enclose an area within a polygon.

Two file formats are used to store vector-based data in ESRI standards:1. Shapefile, and 2. ArcInfo coverage. Shapefile is a simple, non-topological format for storing the geometric location and attribute information of geographic features. It is one of the spatial data formats that can be used in ArcView. [Xiao Liu: Do you want bold and italics here?] The shapefile format defines the geometry and attributes of geographically referenced features in as many as five files with specific file extensions that should be stored in the same project workspace. These file extensions are::

.shp - the file that stores the feature geometry.

.shx - the file that stores the index of the feature geometry.

.dbf - the dBASE file that stores the attribute information of features. When a shapefile is added as a theme to a view, this file is displayed as a feature table.

.sbn and .sbx - the files that store the spatial index of the features. These two files may not exist until one performs on a theme's shape field, theme on theme selection, spatial join, or  index creation. [Xiao Liu, I'm not sure what you mean here.  Hope I haven't changed your meaning.]

 ArcInfo coverage is another digital version of a map forming the basic unit of vector data storage.  In ArcInfo. a coverage contains two kinds of information about geographic objects: geographic information and attribute information. It stores geographic features as primary features (such as arcs, nodes, polygons, and label points) and secondary features (such as tics, map extent, links, and annotation). Associated feature attribute tables describe and store attributes of the geographic features. A coverage usually represents a single theme such as soils, streams, or land use. Unlike Shapefile, which stores in several files, the physical storage of a coverage is in a directory structure containing several ArcInfo internally formatted files for storing tics, boundary, annotation, and attributes.

Raster-based systems, like vector-based systems, also store geographic data, but they view and store surfaces differently. Vector systems define an object and proceed to define its characteristics and attributes, one of whilch is the x, y coordinate location. The raster-based data model is more like a photograph than a map and works in a way similar to a photograph; it is a regular grid of dots (called cells, or pixels) filled with values. In fact, when a picture is stored in a computer, the raster data model is used (ESRI, 1997).

Raster-based systems divide the world into discrete uniform units called cells. Every cell represents a certain specified portion of the earth, such as a square kilometer, hectare or square meter. Each cell is given a value to correspond to the feature or characteristic that is located at or describes the site, such as a drainage basin, soil type, or residential classification. Location is not defined as an attribute but is inherent in the storage structure.

The cell is the primary spatial entity within a grid. Each cell is square, has the same size as other cells in the grid, and contains a numeric value representing the spatial variable at that location. Cell values can be 32-bit integer or real (floating-point) numbers.

The uniform cells are organized into a Cartesian matrix consisting of rows and columns. A row identifies all cells equidistant from the top or bottom (East/West) boundary of a grid. Columns identify all cells equidistant from the left or right (North/South) boundary of the grid. Each Cartesian matrix is called a grid. Every cell in a grid has a unique row and a unique column identifier.

Each grid represents a spatial variable. While vector features are stored as a series of x, y coordinates and topological relationships, grid cells are stored as rows and columns.

The data needed for the implementation of SDSS for coal mine reclamation include soils, streams and lakes, mine-sites, land cover, and topographic maps, as well as Digital Elevation Models (DEM).

The soil layer in this study was digitized from county soil survey maps. The physical, chemical and mineralogical soil properties were correlated to the geographic locations. The attributes included here are soil series, texture, organic matter content, structure, permeability, and pH. Since soil distributes as areas, this layer was stored in vector format. Soil properties were the main source of input for the RUSLE model, and also they were used to generate an acidity map.

The stream layer was provided by the Indiana Geological Survey (IGS). It includes the streams and lakes in the area. Since the stream layer is used primarily for reference of location, no attributes are needed. This layer was stored in vector format.

The mine site layer was provided by IGS also. The attributes associated with mines are mining periods and mining methods. This layer, too, was stored in vector format.

The land cover layer was generated by interpreting remote sensing data and it serves as an input to the RUSLE model. Two XXX sets of Landsat Thematic Mapper (TM) data, acquired in the study area in 1993, are the primary source for classification. Traditionally, there are two major classification approaches used to extract cluster information from remotely sensed data: unsupervised and supervised classification.

Unsupervised classification

Supervised classification

Accuracy assessment

Error matrix
[Xiao Liu, Starting a sentence with an acronym seems a little awkward to me here.  Do you think you ought to spell it out?]
DEM used in this study was acquired from the U.S. Geological Survey (USGS) 7.5 minute DEM data, and was clipped by the study area boundary. The 7.5 minutes DEM data files are digital representations of cartographic information in a raster form. DEMs consist of a sampled array of elevations for a number of ground positions at regularly spaced intervals. Each 7.5 minute DEM is based on a 30 by 30 meter data spacing within a Universal Transverse Mercator UTM projection. It provides the same coverage as the standard USGS 7.5 minute map series.

Topographic maps in the study were the digital version of USGS 7.5- minute topographic maps. These maps had no role in the spatial decision support system. They were not criteria for analysis, nor were they  input for any criteria calculations. While serving as back draft for other layers, topographic maps XXX provide field data for better land use/ land cover classification, and also assist in the visualization of the location information of all the other thematic layers. This layer was stored as TIFF image file with a world file attached to it to identify the location.

[Xiao Liu,  I believe this entire phrase (it's not a sentence) can best be eliminated.]
Considering the property of the whole set of layers that include both raster and vector data. All spatial analysis was carried out in a raster format. The resolution was set at 30 by 30 meter to match the resolution of both TM and USGS DEM data. Since this study deals with regional environmental management, this resolution is reasonably appropriate. All the vector data therein were converted to a raster format for analysis.

The Soil Erosion Rate Model

The erosion rate for a given site results from the combination of many physical and management variables. The (RUSLE) is an erosion model designed to predict the longtime average annual soil loss, carried away by runoff from specific field slopes, in specified cropping and management areas. Widespread use of this equation has substantiated its usefulness and validity for this purpose. It works well too for nonagricultural conditions (Renard et al. 1997).

Along with the RUSLE model, USDA has XXX developed a computer program and three databases for the calculation of XXX six factors. The CITY Database contains information on climate; the CROP Database holds the parameters defining the characteristics of vegetative growth and residue; and, the OPERATIONS Database defines the effects of field operations on the soil, crop, and residues. Some of the values can be derived directly from the database, while others have to be calculated using XXX data from the GIS database.

Multi-criteria decision-making analysis

In this study, MCDM methods were integrated with a GIS to provide a means to place reclamation proceedings in priority order based upon a variety of different choice criteria and on the importance (weight) a decision-maker attaches to these criteria. The decision model selected for this ordering of reclamation proceedings is a combination of weighted summations (Voogd, 1983).

Implementation of the SDSS

The reclamation priorities of coal mine sites are primarily determined by pollution intensity, soil erosion rates, soil properties, and a coal mine site’s proximity to streams. To build a SDSS, all the data must be placed into distinct decision making categories. In this SDSS, three criteria are selected: soil erosion rate, soil acidity, and the proximity of a site to streams.

It is not an easy task to develop a weighting scheme for decision making in mine reclamation. Since the priority of reclamation is decided on by the attractiveness of the sites, according to their values on each criterion, aggregation of the values could generate the rankings.  Therefore, the linear method is adopted in this study. The value of each criteria layer is normalized to a value from 0 to 10 (VCi) depending on the statistics of the original data set or on the decision-makers’ preference.  Each layer is then given a weight (Wi) according to its importance. The output value for each site is, then, the sum of each criteria value multiplied by its weight.

For site j, the output value is

RPj = S ( VCij * Wij ) (i = 0..2) [2]

The erosion rate priority is positively related to the reclamation priorities. This means a high erosion rate gets high priority value. High erosion rate soil has a great potential to be carried away into a stream or channel by running water and, therefore, to degrade the water quality and cause sediment pollution.

Soil acidity can be another major source of pollution. Highly acidic soil may cause severe problems for environmental management and for re-vegetation because of its low nutrient content.

Proximity to streams is the most important factor for reclamation, as pollutants are usually carried by water. It should, therefore, be weighted the most highly in the weighting scheme. This factor is followed by the soil erosion rate factor, and the soil acidity factor. The closer the site is to streams, the higher is the risk that it will spread pollutants from the abandoned sites.

User Interface

The user interface is designed to allow the decision-maker to step through a process that will result in the calculation and display of a map of weighted reclamation priorities. To use this system, the user has to have a knowledge of, or a preference for, each factor. After the user input of the weight of each variable, the system will calculate the final result and generate maps according to the user’s preference. In addition, the user can add new data sets to the system, in order to add a new set of criteria, such as land owner preference, in the multiple criteria list. Modification of the weighting scheme is also possible by interacting with the interface.

It was quite clear that with the task at handX, which was originally that of dealing with spatial data, GIS functions were crucial in building the application system. The implementation of the interface had combined four major parts in its structure: a main frame or programming platform; The GIS functions; a visualization XXX analysis of the programming results; and, a communications capability among all of these.

Borland Delphi by Inprise is a popular package of pregramming software similar to Microsoft Visual Basic and Visual C++. Compared to its peers, Delphi has the best performance/input ratio. Visual Basic, which is predominantly a programming platform, uses very simple syntax in coding and is easy to use.  But the compiled Visual Basic program file is usually large XXX and it runs slowly. Visual C++, on the other hand, is more complicated in conceptX and syntax, though the compiled file runs faster. Delphi lies in between Visual Basic and Visual C++ with XXX respect to speed, size, and programmer’s effort. Another advantage of using Delphi for this research is that several applications have already been built with MapObjects and it definitely helps to get on with the building of applications directly without spending time familiarizing one's self with the programming platform.

GIS functions have to be extracted from GIS software. With a couple of GIS packages and components at hand, a developing environment had to be chosen among the available options. The available GIS software included ArcInfo by ESRI, ArcView by ESRI, and MapObjects by ESRI.

ArcInfo is by far the most powerful GIS software in the GIS world with respect to the functions it provides for both raster and vector analyses. There is no doubt that ArcInfo could fulfil the task of  building a spatial decision support system. The shortcomings of using ArcInfo to develop a SDSS are:

    1. ArcInfo Macro Language (AML) has to be used, which might limit the future modification for other persons who are not familiar with AMLX.
    2. The interface is quite simple but it is not in windows program format.  This might cause confusion for some users XXX who are familiar only with the traditional windows interface.
    3. The display of maps is a little bit more complicated in the ArcInfo environment than in  GIS. XXX. It requires executing some unique commands or functions to set up each display window.
    4. The AML program running in the ArcInfo environment requires a quite large amountX of memory, and it requires many internal commands or functions that cause the application to run slowly.
Despite the disadvantages of building the whole application using this software, ArcInfo provides the necessary GIS functions that the spatial decision support system requires to conduct its  operations. If the functions are not used within the ArcInfo framework, there must be some additional software tool to gain access to the functions from outside ArcInfo.

ArcView is XXX another available GIS package by ESRI. It is a much easier system for XXX users than ArcInfo because of its Windows interface. The functions for vector analysis and process in ArcView are well developed. The advantage of ArcView is that it allows multiple windows displaying different themes or overlays. This makes it very easy for XXX users to conduct XXX comparisons side by side. However, it lacks the capabilities to do raster analysis and grid modeling. To implement the spatial decision support system for this research that mostly required grid analysis, ArcView only could not fulfil it. [Xiao Liu:  I believe this entire sentence needs to be re-cast.  How about the following?] Therefore, ArcView alone could not fulfill an important requirement of this research--that of implementing a spatial decision support system that requires mostly grid anaylsis.

MapObjects is a new product by ESRI using GIS componentware technology. The portability and flexibility of MapObjects for use with other industrial standard packages distinguishes it from both the ArcInfo and the ArcView packageX. Wrapping, the most popularly used GIS function, XXX provides a means for those users who are outside of GIS field to display and analyze geographic information within the framework of a familiar programming environmentX. In implementing the spatial decision support system for this research, MapObjects was chosen to display multiple windows by using the "Map" object provided by the MapObjects program.  Though MapObjects has the capability to display raster or image layers, XXX it is clearly unable to support operations on raster data.

Therefore, since for this study, Delphi was selected to be the programming platform, ArcInfo was selected to be the main GIS function source, and MapObjects was selected to be the visualization tool.  The only step left was to choose a method to connect these three parts. There was no difficulty in embedding MapObjects in the Delphi program because MapObjects was just an Active X control.[Xiao Liu:  This doesn't "compute" to me.  Does it to you?] The remaining question was how to gain access to the ArcInfo functions within the Delphi environment. The Open Development Environment (ODE) by ESRI provides an environment that allows standard programming languages to call ArcInfo functions through Active X controls.  The main ArcInfo components, such as ArcEdit, ArcPlot, and Grid, are encapsulated in the controls. By embedding the controls in the program, the application is able easily to access the commands and functions.

The Open Development Environment (ODE) by ESRI was used as the primary means to develop a user interface to access the ArcInfo GIS functions. This was accomplished through the use of Rapid Application Development (RAD) tools to take advantage of the flexible programming environment, which has easy access to any ArcInfo functions. Since there is a limitation in accessing the ArcInfo through ODE controls designed for Windows NT, MapObjects, which is a GIS component developed by ESRI, was used for displaying multiple windows. There were three major steps required to implement the fully functioning graphic user interface. They involve interface design, function calls, and XXX interface and function. interaction.

The interface is the graphic display of the system on the screen that allows easy viewing by users. Normally it shows the entries to the main functions the system provides. There are several considerations in interface design, such as colors, shapes and layout of the components. Function calls are the underlying processes that are invisible to the users. These are at the core of a SDSS.  Modeling and computing are conducted by XXX function calls. Interaction takes place when a user enters information into the system. The system accepts the user’s request and calls the necessary functions to generate results according to the particular user’s preference.